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Optimization of classifiers for data mining based on combinatorial semigroups
journal contribution
posted on 2011-04-01, 00:00 authored by A V Kelarev, John YearwoodJohn Yearwood, P A WattersThe aim of the present article is to obtain a theoretical result essential for applications of combinatorial semigroups for the design of multiple classification systems in data mining. We consider a novel construction of multiple classification systems, or classifiers, combining several binary classifiers. The construction is based on combinatorial Rees matrix semigroups without any restrictions on the sandwich-matrix. Our main theorem gives a complete description of all optimal classifiers in this novel construction.
History
Journal
Semigroup forumVolume
82Issue
2Pagination
242 - 251Publisher
Springer VerlagLocation
Berlin, GermanyPublisher DOI
ISSN
0037-1912eISSN
1432-2137Language
engPublication classification
C Journal article; C1.1 Refereed article in a scholarly journalCopyright notice
2011, Springer Science+Business Media, LLCUsage metrics
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